Check that all the directories for the .nc files got made
source_dl <- dir(here("data_raw", "CMIP6"))
source_id <- idx$source_id %>% unique() %>% str_to_lower() %>% str_replace_all("-", "_")
stop_if_not(!any(!source_id %in% source_dl))
Check that all the corresponding .csv files exist
csvs <- list.files(here('data'))
stop_if_not(!any(!paste0(source_id, "_data.csv") %in% csvs))
For this analysis, I only want to use models with pr, tas, hfss, and hfls variables in all 5 scenarios (historical, ssp126, ssp245, ssp370, and ssp585)
Perform necessary calculations to compare PET and SPEI among models and between models and observed.
For PET, I’m using the “energy-only” method proposed by Milly and Dune (2016) eq. 8:
\[ PET = 0.8(R_n - G) \]
Except that in their notes, they estimate \(R_n -G\) as hfls + hfss after converting to units of mm/day using the latent heat of vaporazation of water, given by their eq. 2:
\[ L_v(T) = 2.501 - 0.002361T \] in MJ/kg
For the observed data and the CMIP6 data from the same period, I calculate 3-month SPEI using precipitation and PET.
I calculate drought duration as number of consecutive months with SPEI ≤ -1. A single drought, therefore, is defined here as a span of consecutive months all with SPEI ≤ -1.
I’ve ranked the CMIP6 models based on the correlations of monthly precipitation and temperature and the p-value from a non-parametric t-test (Wilcoxon rank sum test) for the mean duration of droughts defined as the nubmer of consecutive months with SPEI ≤ -1.
| Comparison of observed data to CMIP6 'historical' output | ||||||||
|---|---|---|---|---|---|---|---|---|
| Data only from 1980 to 2015 to match observed. | ||||||||
| Source | Monthly means1 | Drought duration and frequency | ||||||
| precipitation | temperature | pr | tas | tasmin | tasmax | duration (mon) mean ± SD2 | number | |
| observed3 | 1.00 | 1.00 | 1.00 | 1.00 | 3.4±2.8 | 24 | ||
| awi_cm_1_1_mr | 0.80 | 0.50 | 0.65 | 0.53 | 2.5±1.3 | 30 | ||
| taiesm1 | 0.71 | 0.51 | NA | NA | 2.9±2.5 | 29 | ||
| fgoals_g3 | 0.92 | 0.56 | 0.48 | 0.76 | 2.2±1.7 | 36 | ||
| fgoals_f3_l | 0.98 | 0.71 | NA | NA | 2±1.4 | 41 | ||
| cas_esm2_0 | 0.85 | 0.61 | 0.83 | 0.71 | 2±1.6 | 40 | ||
| cmcc_esm2 | 0.73 | 0.46 | 0.72 | 0.50 | 2.2±1.5 | 34 | ||
| canesm5 | 0.42 | 0.10 | 0.50 | 0.17 | 3.1±2.5 | 21 | ||
| cmcc_cm2_sr5 | 0.55 | 0.36 | NA | NA | 2.5±2.2 | 37 | ||
| access_esm1_5 | 0.69 | 0.43 | 0.63 | 0.50 | 2.1±1.9 | 40 | ||
| bcc_csm2_mr | 0.13 | 0.38 | 0.62 | 0.43 | 2.2±1.3 | 37 | ||
| ec_earth3_veg_lr | 0.27 | 0.04 | 0.45 | 0.11 | 2.6±2.1 | 38 | ||
| iitm_esm | 0.17 | −0.27 | NA | NA | 2.2±1.3 | 35 | ||
| cams_csm1_0 | 0.36 | −0.27 | NA | NA | 2±1.2 | 37 | ||
| access_cm2 | 0.02 | −0.16 | 0.34 | 0.03 | 2±1.2 | 43 | ||
|
1
Spearman's rho. Rho < 0.45 highlighted in red.
2
Red indicates signifcant Wilcoxon rank sum test (p < 0.05).
3
Observed data from Xavier et al. (2016)
|
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Below are validation reports and plots of all data downloaded from each CMIP6 source.